کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
563154 | 875472 | 2013 | 6 صفحه PDF | دانلود رایگان |

• We propose an improved CBMeMBer filter for multi-target tracking.
• The Gaussian and inverse Gamma mixture distributions are employed.
• Recursively estimate the posterior distributions of measurement noise variances.
• Derive a Gaussian closed-form solution by the variational Bayesian approximation.
Random finite set (RFS) filters have been demonstrating a promising algorithm for tracking an unknown number of targets in real time. However, these methods can only be used in the multi-target tracking systems with known measurement noise variances; otherwise, their tracking performances will decline greatly. To solve this problem, an improved multi-target tracking algorithm is proposed based on the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter and the variational Bayesian (VB) approximation technique to recursively estimate the joint posterior distributions of the multi-target states and the time-varying measurement noise variances. First, the variational calculus method is employed to derive the multi-target estimate recursions, and then the Gaussian and inverse Gamma mixture distributions are introduced to approximate the joint posterior density, and achieve a Gaussian closed-form solution. Simulation results show that the proposed algorithm can effectively estimate the unknown measurement noise variances and has a good performance of multi-target tracking with a strong robustness.
Journal: Signal Processing - Volume 93, Issue 9, September 2013, Pages 2510–2515